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Bioinformatics Advance Access first published online on October 22, 2007
This version published online on October 25, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm497
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Boosting Multiclass Learning with Repeating Codes andWeak Detectors for Protein Subcellular Localization

Chung-Chih Lin 1,2, Yuh-Show Tsai 3, Yu-Shi Lin 4, Tai-Yu Chiu 1, Chia-Cheng Hsiung 3, May-I Lee 3, Jeremy C. Simpson 5 and Chun-Nan Hsu 4,*

1Faculty of Life Sciences and Institute of Genomes, National Yang-Ming University, Taipei, Taiwan, 2Brain Research Center, University System of Taiwan, Hsin-Chu, Taiwan, 3Department of Biomedical Engineering, Chung Yuan Christian University, Jhongli, Taiwan, 4Institute of Information Science, Academia Sinica, Taipei, Taiwan, and 5Department of Cell Biology/Biophysics, EMBL Heidelberg, 69117 Heidelberg, Germany.

*To whom correspondence should be addressed. Dr. Chun-Nan Hsu, E-mail: chunnan{at}iis.sinica.edu.tw


  Abstract

Motivation: Determining locations of protein expression is essential to understand protein function. Advances in green fluorescence protein (GFP) fusion proteins and automated fluorescence microscopy allow for rapid acquisition of large collections of protein localization images. Recognition of these cell images requires an automated image analysis system. Approaches taken by previous work concentrated on designing a set of optimal features and then applying standard machine learning algorithms. In fact, trends of recent advances in machine learning and computer vision can be applied to improve the performance. One trend is the advances in multiclass learning with error-correcting output codes (ECOC). Another trend is the use of a large number of weak detectors with boosting for detecting objects in images of real-world scenes.

Results: We take advantage of these advances to propose a new learning algorithm, AdaBoost.ERC, coupled with weak and strong detectors, to improve the performance of automatic recognition of protein subcellular locations in cell images. We prepared two image data sets of CHO and Vero cells and downloaded a HeLa cell image data set in the public domain to evaluate our new method. We show that AdaBoost.ERC outperforms other AdaBoost extensions. We demonstrate the benefit of weak detectors by showing significant performance improvements over classifiers using only strong detectors. We also empirically test our method's capability of generalizing to heterogeneous image collections. Compared with previous work, our method performs reasonably well for the HeLa cell images.

Availability: CHO and Vero cell images, their corresponding feature sets (SSLF and WSLF), our new learning algorithm, AdaBoost.ERC, and supplementary data are available at http://aiia.iis.sinica.edu.tw/.

Contact: chunnan{at}iis.sinica.edu.tw

Associate Editor: Dr. Jonathan Wren

Received on June 1, 2007; revised on August 28, 2007; accepted on September 30, 2007

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